Currently, no classical clustering algorithm is efficient on its own. The predefined number of clusters required for their operation does not consistently produce satisfactory segmentation results. They exhibit cluster instability, are vulnerable to the local optimum trap, and are sensitive to noise and imaging artefacts. Most contributions designed to overcome these drawbacks incorporate prior knowledge such as cluster label information and statistic measures that demand minimal labelled training data. Although these approaches improve the segmentation accuracy, they tend to diminish the advantages of clustering algorithms over the supervised learning methods. This study proposes a shift from the use of a predefined number of clusters to a clustering tree-based method for performance enhancement of classical clustering algorithms. The proposed method is a three-stage algorithm. It begins with the extraction of low-level features from a clustering tree. Clustering trees are sets of labelled clusters of an image at multiple clustering resolutions. The second stage extracts high-level features by coupling the clustering tree to a single-layer feedforward neural network. The third stage is the classification stage, where the basic model of a neural network extracts the tumour from a high-level feature map.Because neither of the neural networks requires training, the proposed method is both fully unsupervised and fully automated and retains all its advantages over supervised methods. A performance evaluation using FLAIR MRI images of brain tumour patients from the BRATS2015 and BRATS2020 databases demonstrates significant performance enhancement over four classical clustering algorithms and two of the four proposed techniques were comparable to deep learning methods.

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Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi‐class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria.

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X‐ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta‐Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.